Techniques for utilizing patterns and logical entities

ABSTRACT

Systems, devices, and methods discussed herein are directed to utilizing patterns and logical entities to identify and maintain relationships between data assets. In some embodiments, a query comprising a logical entity qualifier, one or more pattern identifiers that indicate a pattern, and a data set identifier may be received. The pattern is executed against a data set corresponding to the data set identifier and one or more logical entities are generated based on this execution. A logical entity may be a label that represents a set of one or more data assets in a data set. Assets that share a label can share attributes that are described by the label. The label corresponding to each logical entity may be presented, where each label represents a different set of data assets which share a common trait. In some embodiments, the user may define a pattern by which commonality may be assessed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalApplication No. 63/091,213, filed Oct. 13, 2020, and entitled,“TECHNIQUES FOR PATTERNS AND LOGICAL ENTITIES,” the content of which isherein incorporated by reference in its entirety.

BACKGROUND

Enterprises collect and handle large amounts of data obtained from avariety of sources. The proliferation of data creates several challengesincluding organization, discoverability, identifying a singular view ofany one data entity or subject area, and relevancy. It can be difficultto understand and/or discover the manner in which data instances relateto one another. Embodiments described herein address these and otherproblems, individually and collectively.

BRIEF SUMMARY

Techniques are provided (e.g., a method, a system, non-transitorycomputer-readable medium storing code or instructions executable by oneor more processors) for utilizing patterns and logical entities toidentify and maintain relationships between data assets. Variousembodiments are described herein, including methods, systems,non-transitory computer-readable storage media storing programs, code,or instructions executable by one or more processors, and the like.

One embodiment is directed to a method for identifying and/ormaintaining relationships between data assets. The method may comprisereceiving, by a computing device, a query comprising a logical entityqualifier, one or more pattern identifiers that indicate a pattern, anda data set identifier. The pattern is executed, by the computing device,against a data set corresponding to the data set identifier. One or morelogical entities (e.g., label(s) that represent a corresponding set ofone or more data assets of the data set) are generated by the computingdevice in real time based at least in part on executing the patternagainst the data set. The one or more labels corresponding to the one ormore logical entities are presented, each label representing a differentset of data assets of the data set.

Another embodiment is directed to a computing device. The computingdevice may comprise a computer-readable medium storing non-transitorycomputer-executable program instructions. The computing device mayfurther comprise a processing device communicatively coupled to thecomputer-readable medium for executing the non-transitorycomputer-executable program instructions. Executing the non-transitorycomputer-executable program instructions with the processing devicecauses the computing device to perform operations. These operations maycomprise receiving, by a computing device, a query comprising a logicalentity qualifier, one or more pattern identifiers that indicate apattern, and a data set identifier. The pattern is executed, by thecomputing device, against a data set corresponding to the data setidentifier. One or more logical entities (e.g., label(s) that representa corresponding set of one or more data assets of the data set) aregenerated by the computing device in real time based at least in part onexecuting the pattern against the data set. The one or more labelscorresponding to the one or more logical entities are presented, eachlabel representing a different set of data assets of the data set.

Yet another embodiment is directed to a non-transitory computer-readablestorage medium storing computer-executable program instructions that,when executed by a processing device of a computing device, cause thecomputing device to perform operations. These operations may comprisereceiving, by a computing device, a query comprising a logical entityqualifier, one or more pattern identifiers that indicate a pattern, anda data set identifier. The pattern is executed, by the computing device,against a data set corresponding to the data set identifier. One or morelogical entities (e.g., label(s) that represent a corresponding set ofone or more data assets of the data set) are generated by the computingdevice in real time based at least in part on executing the patternagainst the data set. The one or more labels corresponding to the one ormore logical entities are presented, each label representing a differentset of data assets of the data set.

The foregoing, together with other features and embodiments will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates a flow for generating one or more logical entities,according to at least one embodiment;

FIG. 2 illustrates an example pattern library, in accordance with atleast one embodiment;

FIG. 3 illustrates an example folder structure across which a number ofdata assets may be distributed, in accordance with at least oneembodiment;

FIG. 4 illustrates an example data set, in accordance with at least oneembodiment;

FIG. 5 illustrates components of a system, according to a particularembodiment;

FIG. 6 is a schematic diagram of an example computer architecture for adata processing engine, including a plurality of modules that mayperform functions in accordance with at least one embodiment;

FIG. 7 depicts a flowchart illustrating an example of a method forgenerating one or more logical entities, in accordance with at least oneembodiment.

FIG. 8 is a block diagram illustrating one pattern for implementing acloud infrastructure as a service system, according to at least oneembodiment.

FIG. 9 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 10 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 11 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 12 is a block diagram illustrating an example computer system,according to at least one embodiment.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofcertain embodiments. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive. The word “exemplary”is used herein to mean “serving as an example, instance, orillustration.” Any embodiment or design described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother embodiments or designs.

The present disclosure relates to a system and techniques foridentifying and/or maintaining relationships between data assetsutilizing patterns and logical entities. A “pattern” refers to asequence of characters of a predefined syntax that define a searchpattern. As used herein, a “logical entity” refers to a label thatidentifies a set of data assets (e.g., files, documents, objects,containers, etc.). In some embodiments, a logical entity may correspondwith a table that maintains a mapping between the label and theparticular set of data assets to which the label refers.

A user may wish to filter out unnecessary data noise while viewing dataassets. For example, while browsing data assets, the user may wish toview only a subset of files that pertain to his particular task.Embodiments herein disclose a method for defining a query using apredefined and/or user-defined library of patterns. In some embodiments,the user may define a pattern using one or more characters from apredefined regular expressions (regex) library. The user may save thispattern as being associated with a pattern identifier and/or patternname which may then be subsequently used to refer to the pattern. Theuser may then subject any suitable number of queries against a data setutilizing any of his predefined patterns. In some embodiments, utilizingthe pattern will cause the system to filter from view any data assetthat does not match the pattern. Thus, the user may utilize thesepatterns to view only the data assets in which he has interest, whilethe remaining data assets are hidden from view.

As another example, the user may utilize newly defined or previouslydefined patterns to generate a number of logical entities. By way ofexample, the user may submit a query that includes a logical entityqualifier (e.g., “LogicalEntity:”, “−Logical Entity”, etc.) and apattern with which the logical entity is to be generated (sometimesreferred to as a “search pattern”). The system may utilize the patternto identify a logical entity (e.g., a label that represents a set ofdata assets that share a common version of the pattern). Any number oflogical entities may be identified across the data set, with eachlogical entity corresponding to a different set of data assets thatshare a common version of the pattern. In some embodiments, a table (oranother suitable storage container) may be utilized to maintainassociations between the logical entity (e.g., the label) and each ofthe data assets in the corresponding set. These logical entities (e.g.,labels) may be presented to the user, enabling the user to ascertaindetails about the data set that would have previously been difficultand/or time consuming to ascertain. The logical entities and thecorresponding data asset sets may be determined and/or updated in realtime. Thus, these logical entities may be updated to reflect currentlabels and/or associated data assets in real time, as changes are madeto the data set.

The disclosed techniques provide improvements over conventional systems.For example, conventional systems may lack the ability for a user toview a data set as a condensed set of labels. By using these labels, theuser may perform a variety of operations using the label to refer to theassociated data assets as a whole. This provides an understanding of thedata set and operations that were previously unavailable to the user.

Moving on to FIG. 1 , which illustrates a flow 100 for generating one ormore logical entities, in accordance with at least one embodiment. Datamanagement computer(s) 102 may be include any suitable number ofcomputing devices that individually and/or collectively maintain a dataset. In some embodiments, the data management computer(s) 102 is adistributed cluster computing system including manager nodes and workernodes, where each nodes is a computing device or a virtual machine. Amanager node may be configured to receive task requests (e.g., fromclient devices, not pictured) and to assign execution of thecorresponding tasks to worker nodes of the data management computer(s)102. Each worker node may operate an executor module (e.g., a softwareor hardware module) that is configured to perform operations in responseto receiving task data from a manager node. In some embodiments, eachworker node and/or manager node of the data management computer(s) 102may be configured to execute a distributed data processing engine.

In some embodiments, the data management computer(s) 102 iscommunicatively coupled to a pattern library 104 that includes one ormore previously user-defined patterns. FIG. 2 illustrates an examplepattern library 200 (e.g., an example of the pattern library 104 of FIG.1 ), in accordance with at least one embodiment. As illustrated, thepattern library 200 is a table with columns 202, 204, 206, 208, 210, and212, although it should be appreciated that such data may be otherwisemaintained in another suitable container such as a list, an object, amapping, or the like. The particular columns illustrated are notnecessary exhaustive, and additional or fewer corresponding data fieldsmay be included for any and/or all of the patterns defined in patternlibrary 200. The pattern library 200 may include any suitable number ofpatterns, each pattern having one or more fragments that identify aportion of the pattern which are identified as being associated with aparticular pattern through column 210. By way of example, a pattern maybe defined utilizing line 214 and fragments 216-222.

Line 214 may be utilized to define an identifier (e.g., 100) and a name(e.g., SomePattern) for a given pattern. Each of the fragments 216-222may be associated with the identifier of the pattern (e.g., 100) viacolumn 210 and reference to a parent identifier (e.g., 100). Thesequence number of a fragment (e.g., corresponding to the correspondingdata field in column 208) may be utilized to identify a sequence betweenfragments. Two or more fragments sharing the same sequence number may beinterpreted as a logical or (e.g., II). Fragment 216 is an example inwhich another previously defined pattern may be referenced (e.g.,pattern 106). Thus, the expression defined by pattern 106 (e.g.,corresponding to, in this case, fragment 224) may be used as theexpression in fragment 216. Fragment 226 of pattern 107 may similarlyreference pattern 106, in this case, using a pattern qualifier (e.g.,“Pattern:”) and the specified name of pattern 106 (e.g., “AnyAlpha”).

Returning to pattern 100, each fragment may identify a portion of thepattern according to the sequence number. Thus, the expanded patterncorresponding to pattern 100 is [a-zA-Z] */yny∥iad/*.json, wherein[a-zA-Z] * is obtained from pattern 106 of pattern library 200. Thepattern “SomePattern” may be used as a query to match data assets suchas tmp/yny/1000.json, tmp/iad/1001.json, but would not matchtmp/hyd/1002.json. The pattern “Cloud-Regions” in expanded form maymatch any data asset that includes “iad”, “yny”, “ams”, “hyd”, or “zrh”.Pattern 103 may be used to match any data asset that includes “hourly”,“daily”, or “monthly”. Patterns 104 and 105 may be similarly utilized todefine patterns for timestamps and a JSON file, respectively.

Returning to FIG. 1 , the patterns of pattern library 104 may bepredefined and stored via any suitable data store. In some embodiments,the data management computer(s) 102 may provide interfaces and/orapplication programming interface(s) (API(s)) for adding and/or removinga pattern to/from the pattern library 104. The example depicted in FIG.1 presumes that the pattern library 104 has already been, at leastpartially defined and previously stored so as to be accessible to thedata management computer(s) 102.

The flow 100 may begin at 106, where a query (e.g., query 108) isreceived (e.g., from user device 110, an example of a laptop, or anysuitable computing device operated by a user). In some embodiments, thequery 108 may be provided via an interface hosted by the data managementcomputer(s) 102 or the query 108 may be received via an applicationprogramming interface or the like. In some embodiments, the query mayinclude any suitable combination of a logical entity qualifier (e.g.,“LogicalEntity:”, “−LE”, or any suitable indicator with which a logicalentity may be indicated), one or more pattern identifiers that indicatea pattern, and data set identifier (e.g., indicating a location of thedata set to be queried). By way of example, the following may bereceived as query 108:

-   {Bucket:%}/{Pattern:Timestamp}_{LogicalEntity:{Pattern:OCI_Regions}_{Pattern:Frequency}_%}·{Pattern:Json    File Type}    where “{Bucket:%} indicates a bucket qualifier coupled with a    wildcard (e.g., %) indicating any pattern. The fragment {Bucket:%}    may be an example of a data set identifier that identifies the data    set as being any suitable path. As another example,    {Bucket:*bling_metering/} may define the data set as being any data    asset within a folder with a path ending in “bling_metering/”. The    pattern qualifier (e.g., “Pattern:”) may be used to refer, by name,    to a previously defined pattern. For example, {Pattern:Timestamp} is    used to refer to pattern 104 of FIG. 2 . {Pattern:Cloud_Regions} is    used to refer to pattern 102 of FIG. 2 . {Pattern:Frequency} is used    to refer to pattern 103 of FIG. 2 . {Pattern:Json File Type} is used    to refer to pattern 105 of FIG. 2 . The logical entity qualifier    (e.g., “LogicalEntity:”) may be used to define a pattern for    generating logical entities (e.g., labels that describe a unique    sets of data assets that similarly match the pattern.

At 112, an expanded query 114 may be generated (e.g., by the datamanagement computer(s) 102) based on the pattern identifiers (e.g.,{Pattern:Timestamp}, {Pattern:Cloud_Regions}, {Pattern:Frequency}, andPattern:Json File Type) of query 108 to retrieve corresponding patternsdefined in pattern library 104.

At 116, the data management computer(s) 102 may execute the expandedquery against a data set corresponding to the data set identifierprovided in query 108. By way of example, the data managementcomputer(s) 102 may execute the expanded query 114 against all or partof the data store 116 based on the pattern defined and associated withthe bucket qualifier. Thus, only data assets that are stored in pathsthat match the pattern defined and associated with the bucket qualifierwill be queried.

FIG. 3 illustrates an example folder structure 300 (or other suitablecontainer such as data store 116 of FIG. 1 ) across which a number ofdata assets may be distributed (e.g., the data assets of data set 400 ofFIG. 4 ), in accordance with at least one embodiment. Each folder maystore any suitable number of data assets, some of which may include oneor more of the data assets of data set 400.

FIG. 4 illustrates an example data set 400, in accordance with at leastone embodiment. The data assets of the data set 400 illustrated in FIG.4 may be initially generated by any suitable method, perhaps as outputfiles to various services of a cloud environment and stored at variouslocations (e.g., the same folder, in differing folders, etc.) withinfolder structure 300 of FIG. 3 (or another suitable folder structure).It should be appreciated that a data set may include more or fewer dataassets from the same or various paths as defined by the patternassociated with the bucket qualifier of a corresponding query. The dataset 400 may, in some embodiments, be a subset of a larger data set(e.g., a data set stored in data store 116 of FIG. 1 ).

It should be appreciated that in some embodiments, data assetscorresponding to a logical entity will share a single namespacehierarchy. For example, the pattern “%/{LogicalEntity:%}/???/YYYY/Mon/*,two logical entities named BikeTrips may be created, one under folder“No_A_Vehicle” and another under folder “Vehicle” even though the dataassets under the relative logical entity path ( . . ./BikeTrips/???/YYYY/Mon/) may share the same pattern (also referred toas a schema). This is due to the data assets being under differentnamespace hierarchies.

Returning to FIG. 1 , at 118, the expanded query 114 is executed againstthe data set stored in data store 116 (having folder structure 300).When executed, the expanded query 114 is used to identify the data set400 based at least in part on matching the expanded query 114 to thefile names of the data assets of data store 116. Thus, data set 400 maydepict the subset of data assets that were identified as matching thepattern defined by the expanded query 114.

At 120, in accordance with the inclusion of the logical entity qualifier(e.g., “LogicalEntity:”) of the query 108, one or more logical entitiesmay be generated based at least in part on the results of executing theexpanded query 114. For example, one or more logical entities may begenerated using the data set 400. By way of example, executing theexpanded query 114 against the data set 400 would create the followinglogical entities:

-   -   yny_hourly_region_res_delayed    -   hyd_hourly_region_res_delayed    -   zrh_hourly_region_res_delayed        where each logical entity may be a label that represents a set        of data assets that match the pattern in a particular manner.        For example, the logical entity “yny_hourly_region_res_delayed”        may represent a set of data assets (e.g., data assets 124) of        data set 400 that include “yny_hourly_region_res_delayed” in the        file name. Similarly, the other logical entities may represent        corresponding sets of data assets (e.g., data assets 126 and        128, respectively). A single table (e.g., table 122), or        separate corresponding tables may be used to store an        association between each logical entity and the corresponding        data assets that the logical entity represents. Thus, each data        asset of the data assets 124 may be associated in the table 122        with the logical entity “yny_hourly_region_res_delayed”, data        assets 126 may be associated in the table 122 with        “hyd_hourly_region_res_delayed”, and data assets 128 may be        associated in the table 122 with        “zrh_hourly_region_res_delayed.” The table 122 (or separate        tables not depicted) may be stored in logical entity data store        129 for subsequent use. By way of example, a subsequent query        may reference logical entities that match a particular pattern        and the reference may be used to access/identify the data assets        associated with the reference logical entity.

At 130, the labels 132 corresponding to the generated logical entities(e.g., “yny_hourly_region_res_delayed”, “hyd_hourly_region_res_delayed”,and “zrh_hourly_region_res_delayed”) may be presented at the user device110 via an interface provided by the data management computer(s) 102.The user is then enabled to view a condensed version of the data set andutilize the labels to reference individual sets of data assetscorresponding to the logical entities. It should be appreciated that theexpanded query 114 may be subsequently executed according to anysuitable predefined schedule, periodicity, or frequency, upon request,upon change of the data set, or at any suitable time. Thus, in someembodiments, the table 122 (or individual tables corresponding to thelogical entities) may be updated in real time as the data set changes.

FIG. 5 illustrates components of a system 500 according to a particularembodiment. In system 500, one or more user(s) 502 may utilize a userdevice (e.g., a user device of a collection of user device(s) 504, eachan example of user device 110 of FIG. 1 ) to provide patterns and/orqueries to data processing engine 544 executing on data managementcomputer(s) 510 (e.g., the data management computer(s) 102 of FIG. 1 ).For example, the user may access a user interface accessible through anapplication 506 running on the user device(s) 504 via one or morenetwork(s) 508. In some aspects, the application 506 operating on theuser device(s) 504 may be hosted, managed, and/or provided by acomputing resources service or service provider, such as by utilizingone or more data management computer(s) 510.

In some examples, the network(s) 508 may include any one or acombination of many different types of networks, such as cable networks,the Internet, wireless networks, cellular networks, and other privateand/or public networks. While the illustrated example represents theuser(s) 502 accessing application functionality over the network(s) 508,the described techniques may equally apply in instances where theuser(s) 502 interact with the data management computer(s) 510 via theone or more user device(s) 504 over a landline phone, via a kiosk, or inany other suitable manner. It should be appreciated that the describedtechniques may apply in other client/server arrangements, as well as innon-client/server arrangements (e.g., locally stored applications,etc.). Additionally, in some embodiments, the data processing engine544, discussed further below in more detail, may operate in whole or inpart on the user device(s) 504. Thus, in some embodiments, the user(s)502 may access the functionality of the data processing engine 544directly through the user device(s) 504 and/or the data managementcomputer(s) 510 via user interfaces provided by the data processingengine 544.

In some embodiments, the application 506 may allow the user(s) 502 tointeract with the data management computer(s) 510. For example, a usermay utilize the application 506 to specify user input defining one ormore patterns. The application 506 may be configured to transmit(electronically convey) the user's input(s) to the data managementcomputer(s) 510, operating at the user device(s) 504 and/or the datamanagement computer(s) 510. The data management computer(s) 510 may inturn be configured to add, remove, or modify a pattern library (e.g.,the pattern library 104 of FIG. 1 ) according to the user input(s). Theapplication 506 may further be configured to receive, process, and/ordisplay output corresponding to adding, removing, or modifying thepattern library. In some embodiments, the application 506 may beconfigured to transmit a user-defined query to the data processingengine 544 for processing and present, via I/O devices 520, any outputgenerated by the data processing engine 544 in response to the query.

The data management computer(s) 510, perhaps arranged in a cluster ofservers or as a server farm, may host the application 506 operating onthe user device(s) 504 and/or cloud-based software services. Otherserver architectures may also be used to host the application 506 and/orcloud-based software services. The application 506 operating on the userdevice(s) 504 may be capable of handling requests from the user(s) 502and serving, in response, various user interfaces that can be renderedat the user device(s) 504. The application 506 operating on the userdevice(s) 504 can present any suitable type of website that supportsuser interaction, including search engine sites and the like. Thedescribed techniques can similarly be implemented outside of theapplication 506, such as with other applications running on the userdevice(s) 504.

The user device(s) 504 may be any suitable type of computing device suchas, but not limited to, a mobile phone, a hand-held scanner, a touchscreen device, a smartphone, a personal digital assistant (PDA), alaptop computer, a desktop computer, a thin-client device, a tablet PC,an electronic book (e-book) reader, etc. In some examples, the userdevice(s) 504 may be in communication with the data managementcomputer(s) 510 via the network(s) 508, or via other networkconnections.

In one illustrative configuration, the user device(s) 504 may include atleast one memory 512 and one or more processing units (or processor(s))514. The processor(s) 514 may be implemented as appropriate in hardware,computer-executable instructions, firmware, or combinations thereof.Computer-executable instruction or firmware implementations of theprocessor(s) 514 may include computer-executable or machine-executableinstructions written in any suitable programming language to perform thevarious functions described.

The memory 512 may store program instructions that are loadable andexecutable on the processor(s) 514, as well as data generated during theexecution of these programs. Depending on the configuration and type ofuser computing device, the memory 512 may be volatile (such as randomaccess memory (RAM)) and/or non-volatile (such as read-only memory(ROM), flash memory, etc.). The user device(s) 504 may also includeadditional removable storage and/or non-removable storage including, butnot limited to, magnetic storage, optical disks, and/or tape storage.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for the computing devices. In someimplementations, the memory 512 may include multiple different types ofmemory, such as static random access memory (SRAM), dynamic randomaccess memory (DRAM), or ROM.

Turning to the contents of the memory 512 in more detail, the memory 512may include an operating system 516, one or more data stores 515, andone or more application programs, modules, or services provided via theapplication 506 (e.g., a browser application, a video playerapplication, etc.). The application 506 may be configured to receive,store, and/or display a network page or other interfaces for interactingwith the data management computer(s) 510. The application 506 mayinclude any suitable video player functionality for playing videocontent as streamed and/or otherwise provided by the data managementcomputer(s) 510. Additionally, the memory 512 may store accesscredentials and/or other user information such as, but not limited to,user IDs, passwords, and/or other user information. In some examples,the user information may include information for authenticating anaccount access request such as, but not limited to, a device ID, acookie, an IP address, a location, or the like.

The user device(s) 504 may also contain communications connection(s) 518that allow the user device(s) 504 to communicate with a stored database,another computing device or server (e.g., the data managementcomputer(s) 510), user terminals and/or other devices on the network(s)508. The user device(s) 504 may also include I/O device(s) 520, such asa keyboard, a mouse, a pen, a voice input device, a touch input device,a display, speakers, a printer, etc.

In some aspects, the data management computer(s) 510 may also be anysuitable type of computing devices such as, but not limited to, a mobilephone, a smart phone, a personal digital assistant (PDA), a laptopcomputer, a desktop computer, a server computer, a thin-client device, atablet PC, etc. Additionally, it should be noted that in someembodiments, the data management computer(s) 510 are executed by onemore virtual machines implemented in a hosted computing environment. Thehosted computing environment may include one or more rapidly provisionedand released computing resources, which computing resources may includecomputing, networking and/or storage devices. A hosted computingenvironment may also be referred to as a cloud-computing environment. Insome examples, the data management computer(s) 510 may be incommunication with the user device(s) 504 and/or other service providersvia the network(s) 508 or via other network connections. The datamanagement computer(s) 510 may include one or more servers, perhapsarranged in a cluster, as a server farm, or as individual servers notassociated with one another. These servers may be configured toimplement the functionality described herein as part of an integrated,distributed computing environment.

In one illustrative configuration, the data management computer(s) 510may include at least one memory 528 and one or more processing units (orprocessor(s)) 530. The processor(s) 530 may be implemented asappropriate in hardware, computer-executable instructions, firmware, orcombinations thereof. Computer-executable instruction or firmwareimplementations of the processor(s) 530 may include computer-executableor machine-executable instructions written in any suitable programminglanguage to perform the various functions described.

The memory 528 may store program instructions that are loadable andexecutable on the processor(s) 530, as well as data generated during theexecution of these programs. Depending on the configuration and type ofdata management computer(s) 510, the memory 528 may be volatile (such asRAM) and/or non-volatile (such as ROM, flash memory, etc.). The datamanagement computer(s) 510 or servers may also include additionalstorage 532, which may include removable storage and/or non-removablestorage. The additional storage 532 may include, but is not limited to,magnetic storage, optical disks and/or tape storage. The disk drives andtheir associated computer-readable media may provide non-volatilestorage of computer-readable instructions, data structures, programmodules, and other data for the computing devices. In someimplementations, the memory 528 may include multiple different types ofmemory, such as SRAM, DRAM, or ROM.

The memory 528, the additional storage 532, both removable andnon-removable, are all examples of computer-readable storage media. Forexample, computer-readable storage media may include volatile ornon-volatile, removable or non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, or other data. Thememory 528 and the additional storage 532 are all examples of computerstorage media. Additional types of computer storage media that may bepresent in the data management computer(s) 510 may include, but are notlimited to, PRAM, SRAM, DRAM, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, DVD or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the data managementcomputer(s) 510. Combinations of any of the above should also beincluded within the scope of computer-readable media.

Alternatively, computer-readable communication media may includecomputer-readable instructions, program modules, or other datatransmitted within a data signal, such as a carrier wave, or othertransmission. However, as used herein, computer-readable storage mediadoes not include computer-readable communication media.

The data management computer(s) 510 may also contain communicationsconnection(s) 534 that allow the data management computer(s) 510 tocommunicate with a stored database, another computing device or server(e.g., content provider computer(s) 511), user terminals and/or otherdevices on the network(s) 508. The data management computer(s) 510 mayalso include I/O device(s) 536, such as a keyboard, a mouse, a pen, avoice input device, a touch input device, a display, speakers, aprinter, etc.

Turning to the contents of the memory 528 in more detail, the memory 528may include an operating system 540, one or more data stores 542, and/orone or more application programs, modules, or services for implementingthe features disclosed herein, such as the features provided by the dataprocessing engine 544. The data processing engine 544 may be configuredto perform the operations discussed above in connection with FIG. 1 .

FIG. 6 is a schematic diagram of an example computer architecture fordata processing engine 600 (e.g., the data processing engine 544 of FIG.5 ), including a plurality of modules 602 that may perform functions inaccordance with at least one embodiment. The modules 602 may be softwaremodules, hardware modules, or a combination thereof. If the modules 602are software modules, the modules 602 can be embodied on a computerreadable medium and processed by a processor in any of the computersystems described herein. It should be noted that any module or datastore described herein, may be, in some embodiments, be a serviceresponsible for managing data of the type required to make correspondingcalculations. The modules 602 may be exist as part of the datamanagement computer(s) 510 of FIG. 5 (or a separate computing resource)operating within the system 500 of FIG. 5 , or the modules may exist asseparate modules or services external to the system 500.

In the embodiment shown in the FIG. 6 , data store 604 (e.g., an exampleof the data store 116 of FIG. 1 ), pattern library 606 (e.g., an exampleof the pattern library 104 of FIG. 1 ), and logical entity data store608 (e.g., an example of the logical entity data store 129 of FIG. 1 )are shown, although content data can be maintained, derived, orotherwise accessed from various data stores, either remote or local todata processing engine 600, to achieve the functions described herein.In at least one embodiment, the data stores described herein may operateas part of the system 500 or may be physically distinct from the system500. Data processing engine 600, as shown in FIG. 6 , includes variousmodules such as input module 612, pattern processing module 614, bucketprocessing module 616, logical entity processing module 618, and anoutput module 620. Some functions of the modules 602 are describedbelow. However, for the benefit of the reader, a brief, non-limitingdescription of each of the modules is provided in the followingparagraphs.

In at least one embodiment, data processing engine 600 includes theinput module 612. Generally, the input module 612 may be utilized toreceive any suitable information with respect to any example providedherein. The input module 612 may include any suitable number ofapplication programming interfaces with which the functionality of dataprocessing engine 600 may be invoked. By way of example, the inputmodule 612 may receive user input (e.g., add, delete, modify requests)corresponding to one or more pattern definitions and/or one or morequeries. The input module 612 may be configured to communicate with theother modules of modules 602 in order to pass data between modulesand/or to invoke the functionality of said modules. In some embodiments,the input module 612 may receive user input adding, removing, and/ormodifying one or more patterns. In accordance with the user input, theinput module 612 may provide the user input to the pattern processingmodule 614 for processing. Similarly, the input module 612 may beconfigured to receive user input specifying a query and may pass theuser input to the pattern processing module 614, the bucket processingmodule 616, and/or the logical entity processing module 618 for furtherprocessing.

The pattern processing module 614 may be configured to execute anysuitable operations corresponding to adding, deleting, and/or modifyingpatterns of the pattern library 104 according to received user input.For example, a user may define pattern 100 of FIG. 2 via user inputprovided at the user device 110 of FIG. 1 . The user input defining thepattern 100 may be stored in the pattern library 606 for subsequent use.In some embodiments, the pattern processing module 614 may execute anysuitable operations for expanding a query (e.g., query 108 of FIG. 1 )to an expanded query (e.g., expanded query 114 of FIG. 1 ) using anysuitable pattern defined in pattern library 606. Thus, pattern namesand/or other identifiers may be included in a query, and the patternprocessing module 614 may generate an expanded query by replacing thesepattern names/identifiers with the corresponding expressions associatedwith those pattern names/identifier as described above in connectionwith FIG. 1 . In some embodiments, the functionality of the bucketprocessing module 616, the logical entity processing module 618, and/orthe output module 620 may be invoked by the pattern processing module614.

The bucket processing module 616 may be configured to identify a bucketqualifier from a received query (and/or an expanded query). For example,the bucket processing module 616 may be configured to set one or morevariables to any suitable value to identify one or more paths (and/orfolders) against which the expanded query may be executed.

The logical entity processing module 618 may be configured to executeany suitable operations to generate one or more logical entitiesaccording to received user input. By way of example, the logical entityprocessing module 618 can parse the query to identify one or morelogical entity qualifiers (e.g., “LogicalEntity:”). Once identified, thelogical entity processing module 618 may maintain a list of logicalentities (e.g., labels) available for the data set. In some embodiments,the logical entity processing module 618 may be configured to generateand/or maintain one or more tables corresponding to the logical entities(e.g., table 122 of FIG. 1 ). In some embodiments, the logical entityprocessing module 618 may maintain a single table for all logicalentities, or one table for each logical entity. Each table may defineassociations between a given logical entity and one or more data assetsthe given logical entity represents. In some embodiments, the table(s)may be stored in logical entity data store 608 by logical entityprocessing module 618. In subsequent queries that reference a previouslyexisting logical entity, the logical entity processing module 618 may beconfigured to access the table(s) in order to identify the data assetsassociated with the provided logical entity such that the operationsprovided in the user input may be performed based at least in part onidentify the set of data assets by logical entity (e.g., by label).

The query module 620 may be configured to execute the expanded queryidentified by the pattern processing module 614 against the data setdefined by the output of bucket processing module 616. Based on theresults obtained from this execution, the query module 620 may invokethe functionality of logical entity processing module 618 to generateone or more logical entities and/or corresponding table(s). In someembodiments, the query module 620 may be configured to receive logicalentity labels/identifiers and/or tables from the logical entityprocessing module 618. The query module 620 may provide query results(e.g., data asset identifiers, logical entity labels and/or tables,etc.) to the output module 622 for processing.

The output module 622 may be configured to output any suitable dataprovided by the other modules 602. For example, the output module 622may receive data asset identifiers, logical entity labels and/or tables,or feedback from adding/removing/modifying a pattern. The output module622 may be configured to output query results, lists of labels, one ormore logical entity tables (e.g., lists of data assets associated withone or more respective logical entities), or the like. In someembodiments, the output module 622 may filter other data assets notidentified from view such that the user is able to define the dataassets that are, for him, viewable based at least in part on thepattern, bucket, and/or logical entity qualifiers used.

FIG. 7 depicts a flowchart illustrating an example of a method 700 forgenerating one or more logical entities, in accordance with at least oneembodiment. In some embodiments, the operations of method 700 may beperformed by the data processing engine 600 of FIG. 6 operating as acomputing service or executing on a computing device within the system500 of FIG. 5 .

The method 700 may begin at block 702, where a query is received by acomputing device (e.g., the data management computer(s) 510 of FIG. 5 ).In some embodiments, the query comprises a logical entity qualifier(e.g., “LogicalEntity:”), one or more pattern identifiers that indicatea pattern (e.g., “Pattern:” indicating inclusion of a pattern), and adata set identifier (e.g., “Bucket:”).

At 704, the computing device (e.g., the query module 620) executes thepattern against a data set corresponding to the data set identifier. Insome embodiments, the pattern processing module 614 of FIG. 6 isutilized to retrieve various portions of the pattern from a patternlibrary (e.g., the pattern library 606 of FIG. 6 ). The bucketprocessing module 616 may be utilized to identify the path and/orlocation of the data set against which the pattern (or a query includingthe pattern) is executed.

At 706, one or more logical entities may be generated by the computingdevice in real time based at least in part on executing the patternagainst the data set. In some embodiments, a logical entity of the oneor more logical entities corresponds to a label that represents a set ofone or more data assets of the data set.

At 708, one or more labels corresponding to the one or more logicalentities may be presented (e.g., at the user device 110 of FIG. 1 ). Insome embodiments, each label presented represents a different set ofdata assets of the data set.

As noted above, infrastructure as a service (IaaS) is one particulartype of cloud computing. IaaS can be configured to provide virtualizedcomputing resources over a public network (e.g., the Internet). In anIaaS model, a cloud computing provider can host the infrastructurecomponents (e.g., servers, storage devices, network nodes (e.g.,hardware), deployment software, platform virtualization (e.g., ahypervisor layer), or the like). In some cases, an IaaS provider mayalso supply a variety of services to accompany those infrastructurecomponents (e.g., billing, monitoring, logging, security, load balancingand clustering, etc.). Thus, as these services may be policy-driven,IaaS users may be able to implement policies to drive load balancing tomaintain application availability and performance.

In some instances, IaaS customers may access resources and servicesthrough a wide area network (WAN), such as the Internet, and can use thecloud provider's services to install the remaining elements of anapplication stack. For example, the user can log in to the IaaS platformto create virtual machines (VMs), install operating systems (OSs) oneach VM, deploy middleware such as databases, create storage buckets forworkloads and backups, and even install enterprise software into thatVM. Customers can then use the provider's services to perform variousfunctions, including balancing network traffic, troubleshootingapplication issues, monitoring performance, managing disaster recovery,etc.

In most cases, a cloud computing model will require the participation ofa cloud provider. The cloud provider may, but need not be, a third-partyservice that specializes in providing (e.g., offering, renting, selling)IaaS. An entity might also opt to deploy a private cloud, becoming itsown provider of infrastructure services.

In some examples, IaaS deployment is the process of putting a newapplication, or a new version of an application, onto a preparedapplication server or the like. It may also include the process ofpreparing the server (e.g., installing libraries, daemons, etc.). Thisis often managed by the cloud provider, below the hypervisor layer(e.g., the servers, storage, network hardware, and virtualization).Thus, the customer may be responsible for handling (OS), middleware,and/or application deployment (e.g., on self-service virtual machines(e.g., that can be spun up on demand) or the like.

In some examples, IaaS provisioning may refer to acquiring computers orvirtual hosts for use, and even installing needed libraries or serviceson them. In most cases, deployment does not include provisioning, andthe provisioning may need to be performed first.

In some cases, there are two different problems for IaaS provisioning.First, there is the initial challenge of provisioning the initial set ofinfrastructure before anything is running. Second, there is thechallenge of evolving the existing infrastructure (e.g., adding newservices, changing services, removing services, etc.) once everythinghas been provisioned. In some cases, these two challenges may beaddressed by enabling the configuration of the infrastructure to bedefined declaratively. In other words, the infrastructure (e.g., whatcomponents are needed and how they interact) can be defined by one ormore configuration files. Thus, the overall topology of theinfrastructure (e.g., what resources depend on which, and how they eachwork together) can be described declaratively. In some instances, oncethe topology is defined, a workflow can be generated that creates and/ormanages the different components described in the configuration files.

In some examples, an infrastructure may have many interconnectedelements. For example, there may be one or more virtual private clouds(VPCs) (e.g., a potentially on-demand pool of configurable and/or sharedcomputing resources), also known as a core network. In some examples,there may also be one or more security group rules provisioned to definehow the security of the network will be set up and one or more virtualmachines (VMs). Other infrastructure elements may also be provisioned,such as a load balancer, a database, or the like. As more and moreinfrastructure elements are desired and/or added, the infrastructure mayincrementally evolve.

In some instances, continuous deployment techniques may be employed toenable deployment of infrastructure code across various virtualcomputing environments. Additionally, the described techniques canenable infrastructure management within these environments. In someexamples, service teams can write code that is desired to be deployed toone or more, but often many, different production environments (e.g.,across various different geographic locations, sometimes spanning theentire world). However, in some examples, the infrastructure on whichthe code will be deployed must first be set up. In some instances, theprovisioning can be done manually, a provisioning tool may be utilizedto provision the resources, and/or deployment tools may be utilized todeploy the code once the infrastructure is provisioned.

FIG. 8 is a block diagram 800 illustrating an example pattern of an IaaSarchitecture, according to at least one embodiment. Service operators802 can be communicatively coupled to a secure host tenancy 804 that caninclude a virtual cloud network (VCN) 806 and a secure host subnet 808.In some examples, the service operators 802 may be using one or moreclient computing devices, which may be portable handheld devices (e.g.,an iPhone®, cellular telephone, an iPad®, computing tablet, a personaldigital assistant (PDA)) or wearable devices (e.g., a Google Glass® headmounted display), running software such as Microsoft Windows Mobile®,and/or a variety of mobile operating systems such as iOS, Windows Phone,Android, BlackBerry 8, Palm OS, and the like, and being Internet,e-mail, short message service (SMS), Blackberry®, or other communicationprotocol enabled. Alternatively, the client computing devices can begeneral purpose personal computers including, by way of example,personal computers and/or laptop computers running various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems.The client computing devices can be workstation computers running any ofa variety of commercially-available UNIX® or UNIX-like operatingsystems, including without limitation the variety of GNU/Linux operatingsystems, such as for example, Google Chrome OS. Alternatively, or inaddition, client computing devices may be any other electronic device,such as a thin-client computer, an Internet-enabled gaming system (e.g.,a Microsoft Xbox gaming console with or without a Kinect® gesture inputdevice), and/or a personal messaging device, capable of communicatingover a network that can access the VCN 806 and/or the Internet.

The VCN 806 can include a local peering gateway (LPG) 810 that can becommunicatively coupled to a secure shell (SSH) VCN 812 via an LPG 810contained in the SSH VCN 812. The SSH VCN 812 can include an SSH subnet814, and the SSH VCN 812 can be communicatively coupled to a controlplane VCN 816 via the LPG 810 contained in the control plane VCN 816.Also, the SSH VCN 812 can be communicatively coupled to a data plane VCN818 via an LPG 810. The control plane VCN 816 and the data plane VCN 818can be contained in a service tenancy 819 that can be owned and/oroperated by the IaaS provider.

The control plane VCN 816 can include a control plane demilitarized zone(DMZ) tier 820 that acts as a perimeter network (e.g., portions of acorporate network between the corporate intranet and external networks).The DMZ-based servers may have restricted responsibilities and help keepsecurity breaches contained. Additionally, the DMZ tier 820 can includeone or more load balancer (LB) subnet(s) 822, a control plane app tier824 that can include app subnet(s) 826, a control plane data tier 828that can include database (DB) subnet(s) 830 (e.g., frontend DBsubnet(s) and/or backend DB subnet(s)). The LB subnet(s) 822 containedin the control plane DMZ tier 820 can be communicatively coupled to theapp subnet(s) 826 contained in the control plane app tier 824 and anInternet gateway 834 that can be contained in the control plane VCN 816,and the app subnet(s) 826 can be communicatively coupled to the DBsubnet(s) 830 contained in the control plane data tier 828 and a servicegateway 836 and a network address translation (NAT) gateway 838. Thecontrol plane VCN 816 can include the service gateway 836 and the NATgateway 838.

The control plane VCN 816 can include a data plane mirror app tier 840that can include app subnet(s) 826. The app subnet(s) 826 contained inthe data plane mirror app tier 840 can include a virtual networkinterface controller (VNIC) 842 that can execute a compute instance 844.The compute instance 844 can communicatively couple the app subnet(s)826 of the data plane mirror app tier 840 to app subnet(s) 826 that canbe contained in a data plane app tier 846.

The data plane VCN 818 can include the data plane app tier 846, a dataplane DMZ tier 848, and a data plane data tier 850. The data plane DMZtier 848 can include LB subnet(s) 822 that can be communicativelycoupled to the app subnet(s) 826 of the data plane app tier 846 and theInternet gateway 834 of the data plane VCN 818. The app subnet(s) 826can be communicatively coupled to the service gateway 836 of the dataplane VCN 818 and the NAT gateway 838 of the data plane VCN 818. Thedata plane data tier 850 can also include the DB subnet(s) 830 that canbe communicatively coupled to the app subnet(s) 826 of the data planeapp tier 846.

The Internet gateway 834 of the control plane VCN 816 and of the dataplane VCN 818 can be communicatively coupled to a metadata managementservice 852 that can be communicatively coupled to public Internet 854.Public Internet 854 can be communicatively coupled to the NAT gateway838 of the control plane VCN 816 and of the data plane VCN 818. Theservice gateway 836 of the control plane VCN 816 and of the data planeVCN 818 can be communicatively couple to cloud services 856.

In some examples, the service gateway 836 of the control plane VCN 816or of the data plane VCN 818 can make application programming interface(API) calls to cloud services 856 without going through public Internet854. The API calls to cloud services 856 from the service gateway 836can be one-way: the service gateway 836 can make API calls to cloudservices 856, and cloud services 856 can send requested data to theservice gateway 836. But, cloud services 856 may not initiate API callsto the service gateway 836.

In some examples, the secure host tenancy 804 can be directly connectedto the service tenancy 819, which may be otherwise isolated. The securehost subnet 808 can communicate with the SSH subnet 814 through an LPG810 that may enable two-way communication over an otherwise isolatedsystem. Connecting the secure host subnet 808 to the SSH subnet 814 maygive the secure host subnet 808 access to other entities within theservice tenancy 819.

The control plane VCN 816 may allow users of the service tenancy 819 toset up or otherwise provision desired resources. Desired resourcesprovisioned in the control plane VCN 816 may be deployed or otherwiseused in the data plane VCN 818. In some examples, the control plane VCN816 can be isolated from the data plane VCN 818, and the data planemirror app tier 840 of the control plane VCN 816 can communicate withthe data plane app tier 846 of the data plane VCN 818 via VNICs 842 thatcan be contained in the data plane mirror app tier 840 and the dataplane app tier 846.

In some examples, users of the system, or customers, can make requests,for example create, read, update, or delete (CRUD) operations, throughpublic Internet 854 that can communicate the requests to the metadatamanagement service 852. The metadata management service 852 cancommunicate the request to the control plane VCN 816 through theInternet gateway 834. The request can be received by the LB subnet(s)822 contained in the control plane DMZ tier 820. The LB subnet(s) 822may determine that the request is valid, and in response to thisdetermination, the LB subnet(s) 822 can transmit the request to appsubnet(s) 826 contained in the control plane app tier 824. If therequest is validated and requires a call to public Internet 854, thecall to public Internet 854 may be transmitted to the NAT gateway 838that can make the call to public Internet 854. Memory that may bedesired to be stored by the request can be stored in the DB subnet(s)830.

In some examples, the data plane mirror app tier 840 can facilitatedirect communication between the control plane VCN 816 and the dataplane VCN 818. For example, changes, updates, or other suitablemodifications to configuration may be desired to be applied to theresources contained in the data plane VCN 818. Via a VNIC 842, thecontrol plane VCN 816 can directly communicate with, and can therebyexecute the changes, updates, or other suitable modifications toconfiguration to, resources contained in the data plane VCN 818.

In some embodiments, the control plane VCN 816 and the data plane VCN818 can be contained in the service tenancy 819. In this case, the user,or the customer, of the system may not own or operate either the controlplane VCN 816 or the data plane VCN 818. Instead, the IaaS provider mayown or operate the control plane VCN 816 and the data plane VCN 818,both of which may be contained in the service tenancy 819. Thisembodiment can enable isolation of networks that may prevent users orcustomers from interacting with other users', or other customers',resources. Also, this embodiment may allow users or customers of thesystem to store databases privately without needing to rely on publicInternet 854, which may not have a desired level of security, forstorage.

In other embodiments, the LB subnet(s) 822 contained in the controlplane VCN 816 can be configured to receive a signal from the servicegateway 836. In this embodiment, the control plane VCN 816 and the dataplane VCN 818 may be configured to be called by a customer of the IaaSprovider without calling public Internet 854. Customers of the IaaSprovider may desire this embodiment since database(s) that the customersuse may be controlled by the IaaS provider and may be stored on theservice tenancy 819, which may be isolated from public Internet 854.

FIG. 9 is a block diagram 900 illustrating another example pattern of anIaaS architecture, according to at least one embodiment. Serviceoperators 902 (e.g. service operators 802 of FIG. 8 ) can becommunicatively coupled to a secure host tenancy 904 (e.g. the securehost tenancy 804 of FIG. 8 ) that can include a virtual cloud network(VCN) 906 (e.g. the VCN 806 of FIG. 8 ) and a secure host subnet 908(e.g. the secure host subnet 808 of FIG. 8 ). The VCN 906 can include alocal peering gateway (LPG) 910 (e.g. the LPG 810 of FIG. 8 ) that canbe communicatively coupled to a secure shell (SSH) VCN 912 (e.g. the SSHVCN 812 of FIG. 8 ) via an LPG 810 contained in the SSH VCN 912. The SSHVCN 912 can include an SSH subnet 914 (e.g. the SSH subnet 814 of FIG. 8), and the SSH VCN 912 can be communicatively coupled to a control planeVCN 916 (e.g. the control plane VCN 816 of FIG. 8 ) via an LPG 910contained in the control plane VCN 916. The control plane VCN 916 can becontained in a service tenancy 919 (e.g. the service tenancy 819 of FIG.8 ), and the data plane VCN 918 (e.g. the data plane VCN 818 of FIG. 8 )can be contained in a customer tenancy 921 that may be owned or operatedby users, or customers, of the system.

The control plane VCN 916 can include a control plane DMZ tier 920 (e.g.the control plane DMZ tier 820 of FIG. 8 ) that can include LB subnet(s)922 (e.g. LB subnet(s) 822 of FIG. 8 ), a control plane app tier 924(e.g. the control plane app tier 824 of FIG. 8 ) that can include appsubnet(s) 926 (e.g. app subnet(s) 826 of FIG. 8 ), a control plane datatier 928 (e.g. the control plane data tier 828 of FIG. 8 ) that caninclude database (DB) subnet(s) 930 (e.g. similar to DB subnet(s) 830 ofFIG. 8 ). The LB subnet(s) 922 contained in the control plane DMZ tier920 can be communicatively coupled to the app subnet(s) 926 contained inthe control plane app tier 924 and an Internet gateway 934 (e.g. theInternet gateway 834 of FIG. 8 ) that can be contained in the controlplane VCN 916, and the app subnet(s) 926 can be communicatively coupledto the DB subnet(s) 930 contained in the control plane data tier 928 anda service gateway 936 (e.g. the service gateway of FIG. 8 ) and anetwork address translation (NAT) gateway 938 (e.g. the NAT gateway 838of FIG. 8 ). The control plane VCN 916 can include the service gateway936 and the NAT gateway 938.

The control plane VCN 916 can include a data plane mirror app tier 940(e.g. the data plane mirror app tier 840 of FIG. 8 ) that can includeapp subnet(s) 926. The app subnet(s) 926 contained in the data planemirror app tier 940 can include a virtual network interface controller(VNIC) 942 (e.g. the VNIC of 842) that can execute a compute instance944 (e.g. similar to the compute instance 844 of FIG. 8 ). The computeinstance 944 can facilitate communication between the app subnet(s) 926of the data plane mirror app tier 940 and the app subnet(s) 926 that canbe contained in a data plane app tier 946 (e.g. the data plane app tier846 of FIG. 8 ) via the VNIC 942 contained in the data plane mirror apptier 940 and the VNIC 942 contained in the data plane app tier 946.

The Internet gateway 934 contained in the control plane VCN 916 can becommunicatively coupled to a metadata management service 952 (e.g. themetadata management service 852 of FIG. 8 ) that can be communicativelycoupled to public Internet 954 (e.g. public Internet 854 of FIG. 8 ).Public Internet 954 can be communicatively coupled to the NAT gateway938 contained in the control plane VCN 916. The service gateway 936contained in the control plane VCN 916 can be communicatively couple tocloud services 956 (e.g. cloud services 856 of FIG. 8 ).

In some examples, the data plane VCN 918 can be contained in thecustomer tenancy 921. In this case, the IaaS provider may provide thecontrol plane VCN 916 for each customer, and the IaaS provider may, foreach customer, set up a unique compute instance 944 that is contained inthe service tenancy 919. Each compute instance 944 may allowcommunication between the control plane VCN 916, contained in theservice tenancy 919, and the data plane VCN 918 that is contained in thecustomer tenancy 921. The compute instance 944 may allow resources, thatare provisioned in the control plane VCN 916 that is contained in theservice tenancy 919, to be deployed or otherwise used in the data planeVCN 918 that is contained in the customer tenancy 921.

In other examples, the customer of the IaaS provider may have databasesthat live in the customer tenancy 921. In this example, the controlplane VCN 916 can include the data plane mirror app tier 940 that caninclude app subnet(s) 926. The data plane mirror app tier 940 can residein the data plane VCN 918, but the data plane mirror app tier 940 maynot live in the data plane VCN 918. That is, the data plane mirror apptier 940 may have access to the customer tenancy 921, but the data planemirror app tier 940 may not exist in the data plane VCN 918 or be ownedor operated by the customer of the IaaS provider. The data plane mirrorapp tier 940 may be configured to make calls to the data plane VCN 918but may not be configured to make calls to any entity contained in thecontrol plane VCN 916. The customer may desire to deploy or otherwiseuse resources in the data plane VCN 918 that are provisioned in thecontrol plane VCN 916, and the data plane mirror app tier 940 canfacilitate the desired deployment, or other usage of resources, of thecustomer.

In some embodiments, the customer of the IaaS provider can apply filtersto the data plane VCN 918. In this embodiment, the customer candetermine what the data plane VCN 918 can access, and the customer mayrestrict access to public Internet 954 from the data plane VCN 918. TheIaaS provider may not be able to apply filters or otherwise controlaccess of the data plane VCN 918 to any outside networks or databases.Applying filters and controls by the customer onto the data plane VCN918, contained in the customer tenancy 921, can help isolate the dataplane VCN 918 from other customers and from public Internet 954.

In some embodiments, cloud services 956 can be called by the servicegateway 936 to access services that may not exist on public Internet954, on the control plane VCN 916, or on the data plane VCN 918. Theconnection between cloud services 956 and the control plane VCN 916 orthe data plane VCN 918 may not be live or continuous. Cloud services 956may exist on a different network owned or operated by the IaaS provider.Cloud services 956 may be configured to receive calls from the servicegateway 936 and may be configured to not receive calls from publicInternet 954. Some cloud services 956 may be isolated from other cloudservices 956, and the control plane VCN 916 may be isolated from cloudservices 956 that may not be in the same region as the control plane VCN916. For example, the control plane VCN 916 may be located in “Region1,” and cloud service “Deployment 8,” may be located in Region 1 and in“Region 2.” If a call to Deployment 8 is made by the service gateway 936contained in the control plane VCN 916 located in Region 1, the call maybe transmitted to Deployment 8 in Region 1. In this example, the controlplane VCN 916, or Deployment 8 in Region 1, may not be communicativelycoupled to, or otherwise in communication with, Deployment 8 in Region2.

FIG. 10 is a block diagram 1000 illustrating another example pattern ofan IaaS architecture, according to at least one embodiment. Serviceoperators 1002 (e.g. service operators 802 of FIG. 8 ) can becommunicatively coupled to a secure host tenancy 1004 (e.g. the securehost tenancy 804 of FIG. 8 ) that can include a virtual cloud network(VCN) 1006 (e.g. the VCN 806 of FIG. 8 ) and a secure host subnet 1008(e.g. the secure host subnet 808 of FIG. 8 ). The VCN 1006 can includean LPG 1010 (e.g. the LPG 810 of FIG. 8 ) that can be communicativelycoupled to an SSH VCN 1012 (e.g. the SSH VCN 812 of FIG. 8 ) via an LPG1010 contained in the SSH VCN 1012. The SSH VCN 1012 can include an SSHsubnet 1014 (e.g. the SSH subnet 814 of FIG. 8 ), and the SSH VCN 1012can be communicatively coupled to a control plane VCN 1016 (e.g. thecontrol plane VCN 816 of FIG. 8 ) via an LPG 1010 contained in thecontrol plane VCN 1016 and to a data plane VCN 1018 (e.g. the data plane818 of FIG. 8 ) via an LPG 1010 contained in the data plane VCN 1018.The control plane VCN 1016 and the data plane VCN 1018 can be containedin a service tenancy 1019 (e.g. the service tenancy 819 of FIG. 8 ).

The control plane VCN 1016 can include a control plane DMZ tier 1020(e.g. the control plane DMZ tier 820 of FIG. 8 ) that can include loadbalancer (LB) subnet(s) 1022 (e.g. LB subnet(s) 822 of FIG. 8 ), acontrol plane app tier 1024 (e.g. the control plane app tier 824 of FIG.8 ) that can include app subnet(s) 1026 (e.g. similar to app subnet(s)826 of FIG. 8 ), a control plane data tier 1028 (e.g. the control planedata tier 828 of FIG. 8 ) that can include DB subnet(s) 1030. The LBsubnet(s) 1022 contained in the control plane DMZ tier 1020 can becommunicatively coupled to the app subnet(s) 1026 contained in thecontrol plane app tier 1024 and to an Internet gateway 1034 (e.g. theInternet gateway 834 of FIG. 8 ) that can be contained in the controlplane VCN 1016, and the app subnet(s) 1026 can be communicativelycoupled to the DB subnet(s) 1030 contained in the control plane datatier 1028 and to a service gateway 1036 (e.g. the service gateway ofFIG. 8 ) and a network address translation (NAT) gateway 1038 (e.g. theNAT gateway 838 of FIG. 8 ). The control plane VCN 1016 can include theservice gateway 1036 and the NAT gateway 1038.

The data plane VCN 1018 can include a data plane app tier 1046 (e.g. thedata plane app tier 846 of FIG. 8 ), a data plane DMZ tier 1048 (e.g.the data plane DMZ tier 848 of FIG. 8 ), and a data plane data tier 1050(e.g. the data plane data tier 850 of FIG. 8 ). The data plane DMZ tier1048 can include LB subnet(s) 1022 that can be communicatively coupledto trusted app subnet(s) 1060 and untrusted app subnet(s) 1062 of thedata plane app tier 1046 and the Internet gateway 1034 contained in thedata plane VCN 1018. The trusted app subnet(s) 1060 can becommunicatively coupled to the service gateway 1036 contained in thedata plane VCN 1018, the NAT gateway 1038 contained in the data planeVCN 1018, and DB subnet(s) 1030 contained in the data plane data tier1050. The untrusted app subnet(s) 1062 can be communicatively coupled tothe service gateway 1036 contained in the data plane VCN 1018 and DBsubnet(s) 1030 contained in the data plane data tier 1050. The dataplane data tier 1050 can include DB subnet(s) 1030 that can becommunicatively coupled to the service gateway 1036 contained in thedata plane VCN 1018.

The untrusted app subnet(s) 1062 can include one or more primary VNICs1064(1)-(N) that can be communicatively coupled to tenant virtualmachines (VMs) 1066(1)-(N). Each tenant VM 1066(1)-(N) can becommunicatively coupled to a respective app subnet 1067(1)-(N) that canbe contained in respective container egress VCNs 1068(1)-(N) that can becontained in respective customer tenancies 1070(1)-(N). Respectivesecondary VNICs 1072(1)-(N) can facilitate communication between theuntrusted app subnet(s) 1062 contained in the data plane VCN 1018 andthe app subnet contained in the container egress VCNs 1068(1)-(N). Eachcontainer egress VCNs 1068(1)-(N) can include a NAT gateway 1038 thatcan be communicatively coupled to public Internet 1054 (e.g. publicInternet 854 of FIG. 8 ).

The Internet gateway 1034 contained in the control plane VCN 1016 andcontained in the data plane VCN 1018 can be communicatively coupled to ametadata management service 1052 (e.g. the metadata management system852 of FIG. 8 ) that can be communicatively coupled to public Internet1054. Public Internet 1054 can be communicatively coupled to the NATgateway 1038 contained in the control plane VCN 1016 and contained inthe data plane VCN 1018. The service gateway 1036 contained in thecontrol plane VCN 1016 and contained in the data plane VCN 1018 can becommunicatively couple to cloud services 1056.

In some embodiments, the data plane VCN 1018 can be integrated withcustomer tenancies 1070. This integration can be useful or desirable forcustomers of the IaaS provider in some cases such as a case that maydesire support when executing code. The customer may provide code to runthat may be destructive, may communicate with other customer resources,or may otherwise cause undesirable effects. In response to this, theIaaS provider may determine whether to run code given to the IaaSprovider by the customer.

In some examples, the customer of the IaaS provider may grant temporarynetwork access to the IaaS provider and request a function to beattached to the data plane tier app 1046. Code to run the function maybe executed in the VMs 1066(1)-(N), and the code may not be configuredto run anywhere else on the data plane VCN 1018. Each VM 1066(1)-(N) maybe connected to one customer tenancy 1070. Respective containers1071(1)-(N) contained in the VMs 1066(1)-(N) may be configured to runthe code. In this case, there can be a dual isolation (e.g., thecontainers 1071(1)-(N) running code, where the containers 1071(1)-(N)may be contained in at least the VM 1066(1)-(N) that are contained inthe untrusted app subnet(s) 1062), which may help prevent incorrect orotherwise undesirable code from damaging the network of the IaaSprovider or from damaging a network of a different customer. Thecontainers 1071(1)-(N) may be communicatively coupled to the customertenancy 1070 and may be configured to transmit or receive data from thecustomer tenancy 1070. The containers 1071(1)-(N) may not be configuredto transmit or receive data from any other entity in the data plane VCN1018. Upon completion of running the code, the IaaS provider may kill orotherwise dispose of the containers 1071(1)-(N).

In some embodiments, the trusted app subnet(s) 1060 may run code thatmay be owned or operated by the IaaS provider. In this embodiment, thetrusted app subnet(s) 1060 may be communicatively coupled to the DBsubnet(s) 1030 and be configured to execute CRUD operations in the DBsubnet(s) 1030. The untrusted app subnet(s) 1062 may be communicativelycoupled to the DB subnet(s) 1030, but in this embodiment, the untrustedapp subnet(s) may be configured to execute read operations in the DBsubnet(s) 1030. The containers 1071(1)-(N) that can be contained in theVM 1066(1)-(N) of each customer and that may run code from the customermay not be communicatively coupled with the DB subnet(s) 1030.

In other embodiments, the control plane VCN 1016 and the data plane VCN1018 may not be directly communicatively coupled. In this embodiment,there may be no direct communication between the control plane VCN 1016and the data plane VCN 1018. However, communication can occur indirectlythrough at least one method. An LPG 1010 may be established by the IaaSprovider that can facilitate communication between the control plane VCN1016 and the data plane VCN 1018. In another example, the control planeVCN 1016 or the data plane VCN 1018 can make a call to cloud services1056 via the service gateway 1036. For example, a call to cloud services1056 from the control plane VCN 1016 can include a request for a servicethat can communicate with the data plane VCN 1018.

FIG. 11 is a block diagram 1100 illustrating another example pattern ofan IaaS architecture, according to at least one embodiment. Serviceoperators 1102 (e.g. service operators 802 of FIG. 8 ) can becommunicatively coupled to a secure host tenancy 1104 (e.g. the securehost tenancy 804 of FIG. 8 ) that can include a virtual cloud network(VCN) 1106 (e.g. the VCN 806 of FIG. 8 ) and a secure host subnet 1108(e.g. the secure host subnet 808 of FIG. 8 ). The VCN 1106 can includean LPG 1110 (e.g. the LPG 810 of FIG. 8 ) that can be communicativelycoupled to an SSH VCN 1112 (e.g. the SSH VCN 812 of FIG. 8 ) via an LPG1110 contained in the SSH VCN 1112. The SSH VCN 1112 can include an SSHsubnet 1114 (e.g. the SSH subnet 814 of FIG. 8 ), and the SSH VCN 1112can be communicatively coupled to a control plane VCN 1116 (e.g. thecontrol plane VCN 816 of FIG. 8 ) via an LPG 1110 contained in thecontrol plane VCN 1116 and to a data plane VCN 1118 (e.g. the data plane818 of FIG. 8 ) via an LPG 1110 contained in the data plane VCN 1118.The control plane VCN 1116 and the data plane VCN 1118 can be containedin a service tenancy 1119 (e.g. the service tenancy 819 of FIG. 8 ).

The control plane VCN 1116 can include a control plane DMZ tier 1120(e.g. the control plane DMZ tier 820 of FIG. 8 ) that can include LBsubnet(s) 1122 (e.g. LB subnet(s) 822 of FIG. 8 ), a control plane apptier 1124 (e.g. the control plane app tier 824 of FIG. 8 ) that caninclude app subnet(s) 1126 (e.g. app subnet(s) 826 of FIG. 8 ), acontrol plane data tier 1128 (e.g. the control plane data tier 828 ofFIG. 8 ) that can include DB subnet(s) 1130 (e.g. DB subnet(s) 1030 ofFIG. 10 ). The LB subnet(s) 1122 contained in the control plane DMZ tier1120 can be communicatively coupled to the app subnet(s) 1126 containedin the control plane app tier 1124 and to an Internet gateway 1134 (e.g.the Internet gateway 834 of FIG. 8 ) that can be contained in thecontrol plane VCN 1116, and the app subnet(s) 1126 can becommunicatively coupled to the DB subnet(s) 1130 contained in thecontrol plane data tier 1128 and to a service gateway 1136 (e.g. theservice gateway of FIG. 8 ) and a network address translation (NAT)gateway 1138 (e.g. the NAT gateway 838 of FIG. 8 ). The control planeVCN 1116 can include the service gateway 1136 and the NAT gateway 1138.

The data plane VCN 1118 can include a data plane app tier 1146 (e.g. thedata plane app tier 846 of FIG. 8 ), a data plane DMZ tier 1148 (e.g.the data plane DMZ tier 848 of FIG. 8 ), and a data plane data tier 1150(e.g. the data plane data tier 850 of FIG. 8 ). The data plane DMZ tier1148 can include LB subnet(s) 1122 that can be communicatively coupledto trusted app subnet(s) 1160 (e.g. trusted app subnet(s) 1060 of FIG.10 ) and untrusted app subnet(s) 1162 (e.g. untrusted app subnet(s) 1062of FIG. 10 ) of the data plane app tier 1146 and the Internet gateway1134 contained in the data plane VCN 1118. The trusted app subnet(s)1160 can be communicatively coupled to the service gateway 1136contained in the data plane VCN 1118, the NAT gateway 1138 contained inthe data plane VCN 1118, and DB subnet(s) 1130 contained in the dataplane data tier 1150. The untrusted app subnet(s) 1162 can becommunicatively coupled to the service gateway 1136 contained in thedata plane VCN 1118 and DB subnet(s) 1130 contained in the data planedata tier 1150. The data plane data tier 1150 can include DB subnet(s)1130 that can be communicatively coupled to the service gateway 1136contained in the data plane VCN 1118.

The untrusted app subnet(s) 1162 can include primary VNICs 1164(1)-(N)that can be communicatively coupled to tenant virtual machines (VMs)1166(1)-(N) residing within the untrusted app subnet(s) 1162. Eachtenant VM 1166(1)-(N) can run code in a respective container1167(1)-(N), and be communicatively coupled to an app subnet 1126 thatcan be contained in a data plane app tier 1146 that can be contained ina container egress VCN 1168. Respective secondary VNICs 1172(1)-(N) canfacilitate communication between the untrusted app subnet(s) 1162contained in the data plane VCN 1118 and the app subnet contained in thecontainer egress VCN 1168. The container egress VCN can include a NATgateway 1138 that can be communicatively coupled to public Internet 1154(e.g. public Internet 854 of FIG. 8 ).

The Internet gateway 1134 contained in the control plane VCN 1116 andcontained in the data plane VCN 1118 can be communicatively coupled to ametadata management service 1152 (e.g. the metadata management system852 of FIG. 8 ) that can be communicatively coupled to public Internet1154. Public Internet 1154 can be communicatively coupled to the NATgateway 1138 contained in the control plane VCN 1116 and contained inthe data plane VCN 1118. The service gateway 1136 contained in thecontrol plane VCN 1116 and contained in the data plane VCN 1118 can becommunicatively couple to cloud services 1156.

In some examples, the pattern illustrated by the architecture of blockdiagram 1100 of FIG. 11 may be considered an exception to the patternillustrated by the architecture of block diagram 1000 of FIG. 10 and maybe desirable for a customer of the IaaS provider if the IaaS providercannot directly communicate with the customer (e.g., a disconnectedregion). The respective containers 1167(1)-(N) that are contained in theVMs 1166(1)-(N) for each customer can be accessed in real-time by thecustomer. The containers 1167(1)-(N) may be configured to make calls torespective secondary VNICs 1172(1)-(N) contained in app subnet(s) 1126of the data plane app tier 1146 that can be contained in the containeregress VCN 1168. The secondary VNICs 1172(1)-(N) can transmit the callsto the NAT gateway 1138 that may transmit the calls to public Internet1154. In this example, the containers 1167(1)-(N) that can be accessedin real-time by the customer can be isolated from the control plane VCN1116 and can be isolated from other entities contained in the data planeVCN 1118. The containers 1167(1)-(N) may also be isolated from resourcesfrom other customers.

In other examples, the customer can use the containers 1167(1)-(N) tocall cloud services 1156. In this example, the customer may run code inthe containers 1167(1)-(N) that requests a service from cloud services1156. The containers 1167(1)-(N) can transmit this request to thesecondary VNICs 1172(1)-(N) that can transmit the request to the NATgateway that can transmit the request to public Internet 1154. PublicInternet 1154 can transmit the request to LB subnet(s) 1122 contained inthe control plane VCN 1116 via the Internet gateway 1134. In response todetermining the request is valid, the LB subnet(s) can transmit therequest to app subnet(s) 1126 that can transmit the request to cloudservices 1156 via the service gateway 1136.

It should be appreciated that IaaS architectures 800, 900, 1000, 1100depicted in the figures may have other components than those depicted.Further, the embodiments shown in the figures are only some examples ofa cloud infrastructure system that may incorporate an embodiment of thedisclosure. In some other embodiments, the IaaS systems may have more orfewer components than shown in the figures, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

In certain embodiments, the IaaS systems described herein may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such an IaaS system is the Oracle Cloud Infrastructure (OCI)provided by the present assignee.

FIG. 12 illustrates an example computer system 1200, in which variousembodiments may be implemented. The system 1200 may be used to implementany of the computer systems described above. As shown in the figure,computer system 1200 includes a processing unit 1204 that communicateswith a number of peripheral subsystems via a bus subsystem 1202. Theseperipheral subsystems may include a processing acceleration unit 1206,an I/O subsystem 1208, a storage subsystem 1218 and a communicationssubsystem 1224. Storage subsystem 1218 includes tangiblecomputer-readable storage media 1222 and a system memory 1210.

Bus subsystem 1202 provides a mechanism for letting the variouscomponents and subsystems of computer system 1200 communicate with eachother as intended. Although bus subsystem 1202 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 1202 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 1204, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 1200. One or more processorsmay be included in processing unit 1204. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 1204 may be implemented as one or more independent processing units1232 and/or 1234 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 1204 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 1204 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)1204 and/or in storage subsystem 1218. Through suitable programming,processor(s) 1204 can provide various functionalities described above.Computer system 1200 may additionally include a processing accelerationunit 1206, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 1208 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system1200 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 1200 may comprise a storage subsystem 1218 thatcomprises software elements, shown as being currently located within asystem memory 1210. System memory 1210 may store program instructionsthat are loadable and executable on processing unit 1204, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 1200, systemmemory 1210 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 1204. In some implementations, system memory 1210 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system1200, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 1210 also illustratesapplication programs 1212, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 1214, and an operating system 1216. By wayof example, operating system 1216 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 12 OS, andPalm® OS operating systems.

Storage subsystem 1218 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem1218. These software modules or instructions may be executed byprocessing unit 1204. Storage subsystem 1218 may also provide arepository for storing data used in accordance with the presentdisclosure.

Storage subsystem 1200 may also include a computer-readable storagemedia reader 1220 that can further be connected to computer-readablestorage media 1222. Together and, optionally, in combination with systemmemory 1210, computer-readable storage media 1222 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 1222 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computing system 1200.

By way of example, computer-readable storage media 1222 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 1222 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 1222 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 1200.

Communications subsystem 1224 provides an interface to other computersystems and networks. Communications subsystem 1224 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 1200. For example, communications subsystem 1224may enable computer system 1200 to connect to one or more devices viathe Internet. In some embodiments communications subsystem 1224 caninclude radio frequency (RF) transceiver components for accessingwireless voice and/or data networks (e.g., using cellular telephonetechnology, advanced data network technology, such as 3G, 4G or EDGE(enhanced data rates for global evolution), WiFi (IEEE 802.11 familystandards, or other mobile communication technologies, or anycombination thereof), global positioning system (GPS) receivercomponents, and/or other components. In some embodiments communicationssubsystem 1224 can provide wired network connectivity (e.g., Ethernet)in addition to or instead of a wireless interface.

In some embodiments, communications subsystem 1224 may also receiveinput communication in the form of structured and/or unstructured datafeeds 1226, event streams 1228, event updates 1230, and the like onbehalf of one or more users who may use computer system 1200.

By way of example, communications subsystem 1224 may be configured toreceive data feeds 1226 in real-time from users of social networksand/or other communication services such as Twitter® feeds, Facebook®updates, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources.

Additionally, communications subsystem 1224 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 1228 of real-time events and/or event updates 1230, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 1224 may also be configured to output thestructured and/or unstructured data feeds 1226, event streams 1228,event updates 1230, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 1200.

Computer system 1200 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 1200 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

Although specific embodiments have been described, variousmodifications, alterations, alternative constructions, and equivalentsare also encompassed within the scope of the disclosure. Embodiments arenot restricted to operation within certain specific data processingenvironments, but are free to operate within a plurality of dataprocessing environments. Additionally, although embodiments have beendescribed using a particular series of transactions and steps, it shouldbe apparent to those skilled in the art that the scope of the presentdisclosure is not limited to the described series of transactions andsteps. Various features and aspects of the above-described embodimentsmay be used individually or jointly.

Further, while embodiments have been described using a particularcombination of hardware and software, it should be recognized that othercombinations of hardware and software are also within the scope of thepresent disclosure. Embodiments may be implemented only in hardware, oronly in software, or using combinations thereof. The various processesdescribed herein can be implemented on the same processor or differentprocessors in any combination. Accordingly, where components or modulesare described as being configured to perform certain operations, suchconfiguration can be accomplished, e.g., by designing electroniccircuits to perform the operation, by programming programmableelectronic circuits (such as microprocessors) to perform the operation,or any combination thereof. Processes can communicate using a variety oftechniques including but not limited to conventional techniques forinter process communication, and different pairs of processes may usedifferent techniques, or the same pair of processes may use differenttechniques at different times.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims. Thus, although specificdisclosure embodiments have been described, these are not intended to belimiting. Various modifications and equivalents are within the scope ofthe following claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected” is to be construed as partly or wholly contained within,attached to, or joined together, even if there is something intervening.Recitation of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate embodiments and does not pose alimitation on the scope of the disclosure unless otherwise claimed. Nolanguage in the specification should be construed as indicating anynon-claimed element as essential to the practice of the disclosure.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is intended to be understoodwithin the context as used in general to present that an item, term,etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y,and/or Z). Thus, such disjunctive language is not generally intended to,and should not, imply that certain embodiments require at least one ofX, at least one of Y, or at least one of Z to each be present.

Preferred embodiments of this disclosure are described herein, includingthe best mode known for carrying out the disclosure. Variations of thosepreferred embodiments may become apparent to those of ordinary skill inthe art upon reading the foregoing description. Those of ordinary skillshould be able to employ such variations as appropriate and thedisclosure may be practiced otherwise than as specifically describedherein. Accordingly, this disclosure includes all modifications andequivalents of the subject matter recited in the claims appended heretoas permitted by applicable law. Moreover, any combination of theabove-described elements in all possible variations thereof isencompassed by the disclosure unless otherwise indicated herein.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In the foregoing specification, aspects of the disclosure are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the disclosure is not limited thereto. Variousfeatures and aspects of the above-described disclosure may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

What is claimed is:
 1. A computer-implemented method, comprising:receiving, by a computing device, a first query comprising a logicalentity qualifier, one or more pattern identifiers that indicate a firstpattern of the first query, and a data set identifier corresponding to adata set comprising a plurality of data assets corresponding to aplurality of electronic files, the logical entity qualifier indicatingexistence of a second pattern within the first query; identifying a setof data assets from the data set based at least in part on executing, bythe computing device against the data set corresponding to the data setidentifier, a second query generated from the first query and comprisingthe first pattern; based at least in part on identifying that the firstquery comprises the logical entity qualifier, generating, by thecomputing device in real time, one or more logical entities based atleast in part on identifying corresponding subsets of the set of dataassets using the second pattern, a logical entity of the one or morelogical entities corresponding to a respective label that represents arespective subset of data assets of the set of data assets, therespective subset of data assets comprising two or more data assets thatsimilarly match the second pattern; maintaining a mapping between theone or more logical entities and the respective subset of data assetsthat each of the one or more logical entities represent; and presenting,by the computing device in response to receiving the first query, acondensed view of the set of data assets identified, the condensed viewcomprising one or more labels corresponding to the one or more logicalentities that were generated, each label representing a different subsetof the set of data assets.
 2. The computer-implemented method of claim1, further comprising: maintaining, by the computing device, a librarydefining one or more patterns, a pattern of the one or more patternsbeing associated with a corresponding pattern identifier of the one ormore pattern identifiers and one or more regular expressions; obtaining,from the library, a plurality of regular expressions based at least inpart on the one or more pattern identifiers of the first query; andgenerating, by the computing device, the pattern based at least in parton the plurality of regular expressions obtained from the library. 3.The computer-implemented method of claim 2, further comprising:detecting, by the computing device, a modification to the pattern basedat least in part to a change in the library defining the one or morepatterns; in response to detecting the modification to the pattern,generating, by the computing device in real time, a new subset of dataassets for a respective logical entity of the one or more logicalentities, the new subset of data assets being identified from the set ofdata assets that were originally identified by executing the secondquery against the data set; and updating the mapping to indicate anassociation between the respective logical entity and the new subset ofdata assets.
 4. The computer-implemented method of claim 1, furthercomprising dynamically updating the mapping over time based at least inpart on changes detected in the data set.
 5. The computer-implementedmethod of claim 1, further comprising: receiving, by the computingdevice, a subsequent query comprising a particular label correspondingto a particular logical entity of the one or more logical entities;identifying, by the computing device from the mapping and the label, oneor more data assets corresponding to the particular logical entity; andexecuting, by the computing device, the subsequent query using the oneor more data assets identified from the mapping and the label.
 6. Acomputing device, comprising: one or more hardware processorscommunicatively coupled to a computer-readable medium; and acomputer-readable medium storing non-transitory computer-executableprogram instructions that, when executed by the one or more hardwareprocessors, causes the computing device to perform operationscomprising: receiving a first query comprising a logical entityqualifier, one or more pattern identifiers that indicate a first patternof the first query, and a data set identifier corresponding to a dataset comprising a plurality of data assets corresponding to a pluralityof electronic files, the logical entity qualifier indicating existenceof a second pattern within the first query; identifying a set of dataassets from the data set based at least in part on executing, againstthe data set corresponding to the data set identifier, a second querygenerated from the first query and comprising the first pattern; basedat least in part on identifying that the first query comprises thelogical entity qualifier, generating, in real time, one or more logicalentities based at least in part on identifying corresponding subsets ofthe set of data assets using the second pattern, a logical entity of theone or more logical entities corresponding to a respective label thatrepresents a respective subset of data assets of the set of data assets,the respective subset of data assets comprising two or more data assetsthat similarly match the second pattern; maintaining a mapping betweenthe one or more logical entities and the respective subset of dataassets that each of the one or more logical entities represent; andpresenting, in response to receiving the first query, a condensed viewof the set of data assets identified, the condensed view comprising oneor more labels corresponding to the one or more logical entities thatwere generated, each label representing a different subset of the set ofdata assets.
 7. The computing device of claim 6, wherein the operationsfurther comprise: maintaining a library defining one or more patterns, apattern of the one or more patterns being associated with acorresponding pattern identifier of the one or more pattern identifiersand one or more regular expressions; obtaining, from the library, aplurality of regular expressions based at least in part on the one ormore pattern identifiers of the first query; and generating the patternbased at least in part on the plurality of regular expressions obtainedfrom the library.
 8. The computing device of claim 7, wherein theoperations further comprise: detecting a modification to the patternbased at least in part to a change in the library defining the one ormore patterns; and in response to detecting the modification to thepattern, generating, in real time, a new subset of data assets for arespective logical entity of the one or more logical entities, the newsubset of data assets being identified from the set of data assets thatwere originally identified by executing the second query against thedata set; and updating the mapping to indicate an association betweenthe respective logical entity and the new subset of data assets.
 9. Thecomputing device of claim 6, wherein the operations further comprisedynamically updating the mapping over time based at least in part onchanges detected in the data set.
 10. The computing device of claim 6,wherein the operations further comprise: receiving, a subsequent querycomprising a particular label corresponding to a particular logicalentity of the one or more logical entities; identifying, from themapping and the label, one or more data assets corresponding to theparticular logical entity; and executing, the subsequent query using theone or more data assets identified from the mapping and the label.
 11. Anon-transitory computer-readable storage medium storingcomputer-executable program instructions that, when executed by ahardware processor of a computing device, cause the computing device toperform operations comprising: receiving a first query comprising alogical entity qualifier, one or more pattern identifiers that indicatea first pattern of the first query, and a data set identifiercorresponding to a data set comprising a plurality of data assetscorresponding to a plurality of electronic files, the logical entityqualifier indicating existence of a second pattern within the firstquery; identifying a set of data assets from the data set based at leastin part on executing, by the computing device against the data setcorresponding to the data set identifier, a second query generated fromthe first query and comprising the first pattern; based at least in parton identifying that the first query comprises the logical entityqualifier, generating, in real time, one or more logical entities basedat least in part on identifying corresponding subsets of the set of dataassets using the second pattern, a logical entity of the one or morelogical entities corresponding to a respective label that represents arespective subset of data assets of the set of data assets, therespective subset of data assets comprising two or more data assets thatsimilarly match the second pattern; and maintaining a mapping betweenthe one or more logical entities and the respective subset of dataassets that each of the one or more logical entities represent;presenting, in response to receiving the first query, a condensed viewof the set of data assets identified, the condensed view comprising oneor more labels corresponding to the one or more logical entities thatwere generated, each label representing a different subset of the set ofdata assets.
 12. The non-transitory computer-readable storage medium ofclaim 11, wherein the operations further comprise: maintaining a librarydefining one or more patterns, a pattern of the one or more patternsbeing associated with a corresponding pattern identifier of the one ormore pattern identifiers and one or more regular expressions; obtaining,from the library, a plurality of regular expressions based at least inpart on the one or more pattern identifiers of the first query; andgenerating the pattern based at least in part on the plurality ofregular expressions obtained from the library.
 13. The non-transitorycomputer-readable storage medium of claim 12, wherein the operationsfurther comprise: detecting a modification to the pattern based at leastin part to a change in the library defining the one or more patterns;and in response to detecting the modification to the pattern,generating, in real time, a new subset of data assets for a respectivelogical entity of the one or more logical entities, the new subset ofdata assets being identified from the set of data assets that wereoriginally identified by executing the second query against the data setand, updating the mapping to indicate an association between therespective logical entity and the new subset of data assets.
 14. Thenon-transitory computer-readable storage medium of claim 11, wherein theoperations further comprise dynamically updating the mapping over timebased at least in part on changes detected in the data set.
 15. Thenon-transitory computer-readable storage medium of claim 11, furthercomprising: receiving a subsequent query comprising a particular labelcorresponding to a particular logical entity of the one or more logicalentities; identifying, from the mapping and the label, one or more dataassets corresponding to the particular logical entity; and executing thesubsequent query using the one or more data assets identified from themapping and the label.
 16. The computer-implemented method of claim 1,wherein identifying the corresponding subsets of the set of data assetsusing the second pattern further comprises: identifying a first subsetof data assets from the set of data assets identified using the secondquery, wherein attributes of the first subset of data assets matches afirst version of the second pattern; and identifying a second subset ofdata assets from the set of data assets identified using the secondquery, wherein attributes of the second subset of data assets matches asecond version of the second pattern, the second version of the secondpattern being different from the first version of the second pattern.17. The computer-implemented method of claim 16, wherein thecomputer-implemented method further comprises: generating a firstlogical entity corresponding to a first label, the first labelrepresenting the first subset of data assets that matched the firstversion of the second pattern; and generating a second logical entitycorresponding to a second label, the second label representing thesecond subset of data assets that matched the second version of thesecond pattern.
 18. The computer-implemented method of claim 17, whereinthe first label indicates the first version of the second pattern, andwherein the second label indicates the second version of the secondpattern.
 19. The computer-implemented method of claim 1, wherein thecondensed view of the set of data assets identified with the secondquery comprises a list of the one or more labels corresponding to theone or more logical entities and excludes listing any data asset of thedata set.
 20. The computer-implemented method of claim 1, wherein thesecond query is periodically executed, wherein periodically executingthe second query causes the mapping to be updated to 1) identifydifferent subsets of data assets for previously generated logicalentities or 2) identify a new subset of data asset corresponding to anewly generated logical entity.